By model bias, we refer to the deviation between the estimated \(\hat{f}\)  and \(f\) .
Inductive bias, i.e. the preference of one generalization over another, is necessary for learning [1]}.
A wrong choice of inductive bias, such as assuming a linear \(\hat{f}\)  for a non-linear \(f\) , leads to deviations of \(\hat{f}\)  from \(f\) .
But there are also other reasons why a bias of \(\hat{f}\)  from \(f\)  may occur, for example a too small training data size.
We discuss the critical assumption of model unbiasedness further in Section .
